The delay in the supply of gasoil has wide Consequences of political, social and economic. Therefore, the gasoil demand forecasting is very important. The use of artificial neural networks in forecasting has many applications. Appropriate design of ANN parameters enhances performance and accuracy of the neuralnetwork model. Trial and error method is used to setting the ANN parameters in the most of studies. Trial and error method is not a reliable solution to achieve the best ANN structure. In the present study, the optimum structure of ANN to forecasting the demand of gasoil in the province of Hormozgan determined by Taguchi experimental design method. Analysis of variance (ANOVA) of the ANN parameters indicates that contribution of the number of neurons in first hidden layer to the changes in the network mean square error (MSE) is about 41% and contribution of the learning algorithm is about 27%.
Also, the results of this study show that artificial neural networks are designed using Taguchi experimental design method have better performance than other Networks.
Morovati Sharifabadi, A., & Khancheh Mehr, R. (2014). Finding the ANN Suitable Structure using Taguchi Experimental Design Method. Journal of Industrial Management Perspective, 4(1), 121-142.
MLA
Ali Morovati Sharifabadi; Rasool Khancheh Mehr. "Finding the ANN Suitable Structure using Taguchi Experimental Design Method". Journal of Industrial Management Perspective, 4, 1, 2014, 121-142.
HARVARD
Morovati Sharifabadi, A., Khancheh Mehr, R. (2014). 'Finding the ANN Suitable Structure using Taguchi Experimental Design Method', Journal of Industrial Management Perspective, 4(1), pp. 121-142.
VANCOUVER
Morovati Sharifabadi, A., Khancheh Mehr, R. Finding the ANN Suitable Structure using Taguchi Experimental Design Method. Journal of Industrial Management Perspective, 2014; 4(1): 121-142.
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